Identification of Metabolic-Associated Genes for the Prediction of Colon and Rectal Adenocarcinoma
Authors Cui Y, Han B, Zhang H, Liu H, Zhang F, Niu R
Received 16 December 2020
Accepted for publication 5 March 2021
Published 31 March 2021 Volume 2021:14 Pages 2259—2277
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Prof. Dr. Takuya Aoki
Yanfen Cui, Baoai Han, He Zhang, Hui Liu, Fei Zhang, Ruifang Niu
Public Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin, 300060, People’s Republic of China
Correspondence: Ruifang Niu; Fei Zhang Tel +86 13752668992
; +86 18622221365
Email [email protected]; [email protected]
Background and Aim: Uncontrolled proliferation is the most prominent biological feature of tumors. In order to rapidly proliferate, tumor cells regulate their metabolic behavior by controlling the expression of metabolism-related genes (MRGs) to maximize the utilization of available nutrients. In this study, we aimed to construct prognosis models for colorectal adenocarcinoma (COAD) and rectum adenocarcinoma (READ) using MRGs to predict the prognoses of patients.
Methods: We first acquired the gene expression profiles of COAD and READ from the TCGA database, and then utilized univariate Cox analysis, Lasso regression, and multivariable Cox analysis to identify the MRGs for risk models.
Results: Eight genes (CPT1C, PLCB2, PLA2G2D, GAMT, ENPP2, PIP4K2B, GPX3, and GSR) in the colon cancer risk model and six genes (TDO2, PKLR, GAMT, EARS2, ACO1, and WAS) in the rectal cancer risk model were identified successfully. Multivariate Cox analysis indicated that these two models could accurately and independently predict overall survival (OS) for patients with COAD or READ. Furthermore, functional enrichment analysis was used to identify the metabolism pathway of MRGs in the risk models and analyzed these genes comprehensively. Then, we verified the prognosis model in independent COAD cohorts (GSE17538) and detected the correlations of the protein expression levels of GSR and ENPP2 with prognosis for COAD or READ.
Conclusion: In this study, 14 MRGs were identified as potential prognostic biomarkers and therapeutic targets for colorectal cancer.
Keywords: metabolism-related gene, colon adenocarcinoma, rectum adenocarcinoma, prognosis, ENPP2, GSR
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